scisuit 1.3.4

Scientific Computing Package

scisuit

A computing and visualization library designed with engineers in mind..

 

Available Libraries

1. Plotting,
2. Engineering,
3. Statistics,
4. Roots,
5. Integration,
6. Fitting,
7. Optimization

 

Plot Library

Interactive charts (Bar, Box-Whisker, Bubble, Direction Field, Histogram, Moody, Psychrometry, QQnorm, QQplot, Quiver, Scatter). Using the plot.gdi library existing charts can be extended or new visualizations can be created.

A simple scatter chart example:

import numpy as np
import scisuit.plot as plt 

x = np.arange(1, 6)
y = x**2 - 2*x + 5

plt.scatter(x=x, y=y)
plt.show()

   

Engineering Library

Designed mostly for process engineers.

Examples

1. Psychrometry:

Computation of properties of humid-air.

from scisuit.eng import psychrometry

r = psychrometry(P=101, Tdb=30, Twb=20)

#all of the properties
print(r)

P=101.0 kPa,
Tdb=30.0 C
Twb=20.0 C
Tdp=14.17 C
H=57.06 kJ/kg da
RH=39.82 %
W=0.0106 kg/kg da
V=0.876 m3/kg da

 

2. Food:

A rich class for not only computation of food properties but also to perform food arithmetic.

import scisuit.eng as eng

milk = eng.Food(water=88.13, protein=3.15, cho=4.80, lipid=3.25, ash=0.67)
water = eng.Food(water=100)

#removal of 87% water from milk
powder = milk - 0.87*water 
print(powder)

Type = Food
Weight (unit weight) = 0.13
Temperature (C) = 20.0
water (%) = 8.69
cho (%) = 36.92
protein (%) = 24.23
lipid (%) = 25.0
ash (%) = 5.15
aw = 0.194

   

Statistics Library

Many statistical tests & distributions.

import scisuit.stats as st

#Normal distribution
st.dnorm(0.1, mean=1, sd=2)
st.pnorm(0.1, mean=1, sd=2)


#Binomial distribution
st.dbinom(x=[7, 8, 9], size=9, prob=0.94))

#Weibull distribution
st.dweibull(x=3, shape=2, scale=4)

#log-normal distribution
st.dlnorm(0.1, meanlog=1, sdlog=2)
st.plnorm(0.1, meanlog=1, sdlog=2)

   

Numerics Library

Procedures for root finding, fitting, integration...

from scisuit.roots import bisect, brentq, Info

def func(x):
    return x**2-5

root, info = bisect(f=func, a=0, b=5)

print("**** Bisection method ****")
print(root," ", info)

root, info = brentq(f=func, a=0, b=5)

print("\n **** Brent's method ****")
print(root," ", info)

**** Bisection method ****
2.2360706329345703   Info(err=9.5367431640625e-06, iter=19, conv=True, msg='')

 **** Brent's method ****
2.2360684081902256   Info(err=None, iter=8, conv=True, msg='')